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DEA target setting using lexicographic and endogenous directional distance function approaches

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  • Sebastián Lozano

    (University of Seville)

  • Narges Soltani

    (Kharazmi University)

Abstract

Directional Distance Function (DDF) is an approach often used in data envelopment analysis (DEA) due to its clear interpretation and to the flexibility provided by the possibility of choosing the projection direction towards the efficient frontier. In this paper two new DDF approaches are considered. The first one uses an exogenous directional vector and a multi-stage methodology that at each step uses the projection along the input and output dimensions of the directional vector that can be improved. This lexicographic DDF approach also computes a directional efficiency score and a directional inefficiency indicator for each input and output variable. The second approach is a non-linear optimization model that endogenously determines the directional vector so that the smallest improvement required to reach the efficient frontier is computed.

Suggested Citation

  • Sebastián Lozano & Narges Soltani, 2018. "DEA target setting using lexicographic and endogenous directional distance function approaches," Journal of Productivity Analysis, Springer, vol. 50(1), pages 55-70, October.
    • Handle: RePEc:kap:jproda:v:50:y:2018:i:1:d:10.1007_s11123-018-0534-x
    DOI: 10.1007/s11123-018-0534-x
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    Cited by:

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    3. Yu, Ming-Miin & Rakshit, Ipsita, 2023. "Target setting for airlines incorporating CO2 emissions: The DEA bargaining approach," Journal of Air Transport Management, Elsevier, vol. 108(C).
    4. Kao, Chiang, 2022. "A maximum slacks-based measure of efficiency for closed series production systems," Omega, Elsevier, vol. 106(C).

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